EE 230: MATHEMATICAL METHODS FOR ENGINEERS
University of California, Riverside
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): graduate standing; or consent of instructor. Covers fundamental concepts for advanced study in electrical engineering, robotics, machine learning, and data science. Includes vector spaces; partitioned, unitary, and positive definite matrices; differential calculus with matrices; matrix decompositions; non-diagonalizable matrices; solution of linear equation systems; gradient descent and Newton’s method; introduction to linear optimization; the Lagrangian method. May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.
Average GPA: 3.36
Grade distribution records: 255 students across 7 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 20 | 7.8% |
| A | 77 | 30.2% |
| A- | 32 | 12.5% |
| B+ | 31 | 12.2% |
| B | 47 | 18.4% |
| B- | 13 | 5.1% |
| C+ | 9 | 3.5% |
| C | 14 | 5.5% |
| C- | 2 | 0.8% |
| D | 3 | 1.2% |
| F | 2 | 0.8% |
| NP | 2 | 0.8% |
| S | 3 | 1.2% |
Based on 255 student grade records across 7 terms and 1 professor.
Instructors
- Ran Cheng 255 students, Average GPA 3.36